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A Hybrid Machine Learning Fuzzy Non-linear Regression Approach for Neutrosophic Fuzzy Set
Research Article | Published: 02 June 2025
IECE Transactions on Machine Intelligence Volume 1, Issue 1, 2025: 42-51
Volume 1, Issue 1, IECE Transactions on Machine Intelligence
Volume 1, Issue 1, 2025
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IECE Transactions on Machine Intelligence, Volume 1, Issue 1, 2025: 42-51

Research Article | 02 June 2025
A Hybrid Machine Learning Fuzzy Non-linear Regression Approach for Neutrosophic Fuzzy Set
by
Mufala Khan Mufala Khan 1
Rakesh Kumar Rakesh Kumar 1 *
Gaurav Dhiman Gaurav Dhiman 2,3
1 Department of Mathematics, Lovely Professional University, Phagwara 144411, Punjab, India
2 MEU Research Unit, Middle East University, Amman, Jordan
3 School of Sciences and Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Punjab, India
* Corresponding Author: Rakesh Kumar, [email protected]
DOI: 10.62762/TMI.2025.561363
Received: 27 March 2025, Accepted: 23 May 2025, Published: 02 June 2025  
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Abstract
Neutrosophic sets play a significant role for handling indeterminacy. In this paper, we introduce a novel fuzzy non-linear regression model to find the minimum spread of neutrosophic fuzzy sets. Kuhn-Tucker's necessary conditions are employed to estimate the parameters for non-linear regression models, which can be applied to any data set. The resulting hybrid model possesses the ability to minimise the spread of uncertainty in a much better fashion than the existing non-linear regression contenders which rely on KKT- based model. The hybrid approach reduces the maximum spread by 22.09% and improves prediction accuracy, as shown by a 22.23% reduction in RMSE. The study’s findings highlight the hybrid model’s ability to achieve tighter spreads and enhanced predictive reliability, particularly in complex systems where uncertainties in data are significant. This research contributes to advancing fuzzy regression techniques, offering a powerful tool for improved uncertainty quantification in nonlinear systems.
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A Hybrid Machine Learning Fuzzy Non-linear Regression Approach for Neutrosophic Fuzzy Set
Keywords
fuzzy sets
regression analysis
fuzzy non-linear regression model
neutrosophic fuzzy set
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Abo-Sinna, M. A., & Ragab, N. G. (2023). Neutrosophic non-linear regression based on Kuhn-Tucker necessary conditions. Journal of Statistics Applications & Probability, 12(1), 49-59.
    [Google Scholar]
  2. Smarandache, F. (2005). Neutrosophic set—a generalization of the intuitionistic fuzzy set. International Journal of Pure and Applied Mathematics, 24(3), 287-297.
    [Google Scholar]
  3. Ye, J., & Zhang, Q. (2014). Single valued neutrosophic similarity measures for multiple attribute decision-making. Neutrosophic sets and systems, 2, 48-54.
    [Google Scholar]
  4. Reig-Mullor, J., & Salas-Molina, F. (2022). Non-linear neutrosophic numbers and its application to multiple criteria performance assessment. International Journal of Fuzzy Systems, 24(6), 2889-2904.
    [CrossRef]   [Google Scholar]
  5. Das, S., Roy, B. K., Kar, M. B., Kar, S., & Pamučar, D. (2020). Neutrosophic fuzzy set and its application in decision making. Journal of Ambient Intelligence and Humanized Computing, 11, 5017-5029.
    [CrossRef]   [Google Scholar]
  6. Garg, H., & Nancy. (2018). Non-linear programming method for multi-criteria decision making problems under interval neutrosophic set environment. Applied Intelligence, 48, 2199-2213.
    [CrossRef]   [Google Scholar]
  7. Darehmiraki, M. (2024). Neutrosophic Fuzzy Regression: A Linear Programming Approach. Iranian Journal of Operations Research, 15(1), 1-11.
    [Google Scholar]
  8. Qiu, J., Jiang, L., Fan, H., Li, P., & You, C. (2024). Dynamic nonlinear simplified neutrosophic sets for multiple-attribute group decision making. Heliyon, 10(5), e27345.
    [CrossRef]   [Google Scholar]
  9. Aydin, N., Seker, S., Deveci, M., & Zaidan, B. B. (2024). Post-earthquake debris waste management with interpretive-structural-modeling and decision-making-trial, and evaluation-laboratory under neutrosophic fuzzy sets. Engineering Applications of Artificial Intelligence, 138, 109251.
    [CrossRef]   [Google Scholar]
  10. Smarandache, F., Ali, A. M., & Abdelhafeez, A. (2024). Single Valued Neutrosophic HyperSoft Set based on VIKOR Method for 5G Architecture Selection. Infinite Study.
    [Google Scholar]
  11. Smarandache, F., Abdel-Basset, M., & Broumi, S. (2024). Neutrosophic Sets and Systems, Vol. 72, 2024. Neutrosophic Sets and Systems, 72(1), 24.
    [Google Scholar]
  12. Vennila, B., & Sweety, C. A. C. (2024). Analyzing an M/M/c Queue with Linguistic Single-Valued Neutrosophic Logic Using Parametric Nonlinear Programming. In Data-Driven Modelling with Fuzzy Sets (pp. 35-65). CRC Press.
    [Google Scholar]
  13. Edalatpanah, S. A., Hassani, F. S., Smarandache, F., Sorourkhah, A., Pamucar, D., & Cui, B. (2024). A hybrid time series forecasting method based on neutrosophic logic with applications in financial issues. Engineering Applications of Artificial Intelligence, 129, 107531.
    [CrossRef]   [Google Scholar]
  14. Rajesh, K., Rathod, S., Kundale, J., Rathod, N., Anand, M., Saikia, U., ... & Martin, N. (2024). A Study on Interval Valued Temporal Neutrosophic Fuzzy Sets. International Journal of Neutrosophic Science (IJNS), 23(1).
    [CrossRef]   [Google Scholar]
  15. Smarandache, F. (2024). Foundation of superhyperstructure & neutrosophic superhyperstructure. Neutrosophic Sets and Systems, 63, 367-381.
    [Google Scholar]
  16. Alqaysi, M. E., Albahri, A. S., & Hamid, R. A. (2024). Evaluation and benchmarking of hybrid machine learning models for autism spectrum disorder diagnosis using a 2-tuple linguistic neutrosophic fuzzy sets-based decision-making model. Neural Computing and Applications, 36(29), 18161-18200.
    [CrossRef]   [Google Scholar]
  17. El-Hefenawy, N., Metwally, M. A., Ahmed, Z. M., & El-Henawy, I. M. (2016). A review on the applications of neutrosophic sets. Journal of Computational and Theoretical Nanoscience, 13(1), 936-944.
    [CrossRef]   [Google Scholar]
  18. Vázquez, M. L., & Smarandache, F. (2024). A Neutrosophic Approach to Study Agnotology: A Case Study on Climate Change Beliefs. HyperSoft Set Methods in Engineering, 2, 1-8.
    [Google Scholar]
  19. Salama, A. A., & Alblowi, S. A. (2012). Neutrosophic set and neutrosophic topological spaces. Journal of Mathematics and Computer Science, 2(1), 31-35.
    [Google Scholar]
  20. Borah, G., & Dutta, P. (2024). Fuzzy risk analysis in crop selection using information measures on quadripartitioned single-valued neutrosophic sets. Expert Systems with Applications, 255, 124750.
    [CrossRef]   [Google Scholar]
  21. Guo, Y., & Cheng, H. D. (2020). New neutrosophic approach to image segmentation. Pattern Recognition, 42(5), 587-595.
    [CrossRef]   [Google Scholar]
  22. Khan, M., Kumar, R., & Sharma, Y. (2024). Application of computing in data generation in molybdenum disulphide using fuzzy regression approach. International Journal on Interactive Design and Manufacturing, 18, 6205-6214.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Khan, M., Kumar, R., & Dhiman, G. (2025). A Hybrid Machine Learning Fuzzy Non-linear Regression Approach for Neutrosophic Fuzzy Set. IECE Transactions on Machine Intelligence, 1(1), 42–51. https://doi.org/10.62762/TMI.2025.561363
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